期刊文献+

利用局部离散度活动轮廓模型的强度非均匀图像分割

Local Dispersion Active Contour Model for Image Segmentation with Intensity Inhomogeneity
下载PDF
导出
摘要 强度非均匀现象在真实图像中普遍存在,采用常规基于强度的分割算法会导致严重的误分割。针对强度非均匀图像分割,提出了基于局部离散度的活动轮廓模型分割算法。首先定义基于类内类间距离的离散度,然后利用核函数提取局部区域信息,同时加入边缘指示函数加权的轮廓线长度项能量,建立基于局部离散度的活动轮廓模型。最后引入水平集函数惩罚项,避免水平集方法在演化求解时需要不断初始化的问题。合成图像和真实图像实验结果证明本文算法性能稳定,适应于强度非均匀图像的分割。 Image segmentation is an important procedure in image processing and computer vision,active contour model methods have been widely used in image segmentation. Intensity inhomogeneities often occur in real-world images,and it may lead to serious misclassifications by intensity-based segmentation algorithms that assume a uniform intensity. In order to overcome the difficulties,a local dispersion-based active contour model for image segmentation is proposed. Firstly,the dispersion energy is defined in terms of the within-class distance and between-class distance. Secondly,with a kernel function,the dispersion information in local regions is extracted to establish the local dispersion-based active contour model,and a curve length energy term that weights by an edge indicator function is also incorporated into the novel model. Finally,a penalty term is added to avoid reinitializing periodically during the evolution of the level set method. Experimental results for both synthetic images and real images show desirable performances of the proposed method.
出处 《信号处理》 CSCD 北大核心 2016年第3期335-340,共6页 Journal of Signal Processing
基金 国家自然科学基金(41301481)
关键词 活动轮廓模型 图像分割 强度非均匀 类内类间距离 局部离散度 active contour model image segmentation intensity inhomogeneity within-class and between-class distance local dispersion
  • 相关文献

参考文献15

  • 1Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models[J]. InternationalJournal of Computer Vision, 1988, 1(4) :321-331.
  • 2Song H, Huang B, Zhang K. A globally statistical active contour model for segmentation of oil slick in SAR imagery[J]. IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(6): 2402-2409.
  • 3YinJ, YangJ. A modified level set approach for segmentation of multi band polarimetric SAR images[J]. IEEE Transaction on Geoscience and Remote Sensing, 2014, 52( 11) : 7222-7232.
  • 4Mukherjee S, Acton S T. Region based segmentation in presence of intensity inhomogeneity using legendre polynomials[J]. IEEE Signal Processing Letters, 2015,22 (3) :298-302.
  • 5Wang B, Gao X, LiJ. et al. A level set method with shape priors by using locality preserving projections[J] . Neurocomputing, 2015,170: 188-200.
  • 6李敏,梁久祯,廖翠萃.基于聚类信息的活动轮廓图像分割模型[J].模式识别与人工智能,2015,28(7):665-672. 被引量:11
  • 7Caselles V, Kimmel R, Sapiro G. Geodesic active contours[C]// Fifth International Conference on Computer Vision, 1995:694-699.
  • 8Chan T, Vese L. Active Contours without Edges[J] . IEEE Transaction on Image Processing, 2001, 10 (2) : 266-277.
  • 9Ii C, Kao C Y, GoreJ C, et al. Minimization of regionscalable fitting energy for image segmentation[J] . IEEE Transaction on Image Processing, 2008, 17(10) :1940-1949.
  • 10林挺强,高峰,唐沐恩,文贡坚.一种新的基于CV模型的图像分割算法[J].信号处理,2010,26(12):1852-1857. 被引量:11

二级参考文献58

  • 1王文杰,封建湖.基于变分Level Set方法的图像分割[J].计算机工程与应用,2006,42(18):68-70. 被引量:3
  • 2Chan TF,Vese LA.Active contours without edges[J].IEEE Transactions on Image Processing.2001,10(2):266-277.
  • 3Mumford D,Shah J,Optimal approximation by piecewise smooth functions and associated variational problems[J].Commun.Pure Appl.Math,1989,vol.42,pp.577-685.
  • 4Osher S,Sethian J A.Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi Formulation[J].Journal of Computational Physics.1998,79(1):12-49.
  • 5Serbian J A,A fast marching level set method for monotoically advancing fronts[J].In Proc,Nat,Ac,Science,1996,93:1591-1694.
  • 6Chop D.Computing minimal surfaces via Level Set curvature flow[J].Journal of Computational Physics,1993,106:77-91.
  • 7Adalsteinsson D,Sethian J A.A fast level set method for propagating interface[J].Journal of Computational Physics,1995,118:269-277.
  • 8Li C,Xu C,Gui C,Fox.M.D.Level Set Evolution Without Reinitialization:A New Variational Formulation[A].IEEE International Conference on Computer Vision and Pattern Recognition.San Diego:IEEE,2005,1:430-436.
  • 9Osher S,Fedkiw R.Level Set Methods and Dynamic Implicit Surfaces[M].New York:Springer,2003:29-30.
  • 10Bresson X,Esedoglu S,Vandergheynst P,et al.Fast global minimization of the active contour/snake model.[J]Journal of Mathematical Imaging Vision.2007,28(1):151-167.

共引文献43

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部